1-D CNN-Based Online Signature Verification with Federated Learning

TL;DR

This paper introduces a federated learning framework using 1-D CNNs for online signature verification, improving privacy, scalability, and efficiency while maintaining high accuracy and low error rates.

Contribution

The paper presents a novel federated learning approach with 1-D CNNs specifically designed for online signature verification, addressing privacy and scalability issues.

Findings

Achieves an EER of 3.33% with centralized model

Federated configurations maintain high accuracy with multiple agents

Framework reduces local computational resources

Abstract

Online signature verification plays a pivotal role in security infrastructures. However, conventional online signature verification models pose significant risks to data privacy, especially during training processes. To mitigate these concerns, we propose a novel federated learning framework that leverages 1-D Convolutional Neural Networks (CNN) for online signature verification. Furthermore, our experiments demonstrate the effectiveness of our framework regarding 1-D CNN and federated learning. Particularly, the experiment results highlight that our framework 1) minimizes local computational resources; 2) enhances transfer effects with substantial initialization data; 3) presents remarkable scalability. The centralized 1-D CNN model achieves an Equal Error Rate (EER) of 3.33% and an accuracy of 96.25%. Meanwhile, configurations with 2, 5, and 10 agents yield EERs of 5.42%, 5.83%, and 5.63%, along with accuracies of 95.21%, 94.17%, and 94.06%, respectively.